Hierarchical Network Data Analytics Framework for B5G Network Automation: Design and Implementation

TL;DR

This paper proposes a hierarchical network data analytics framework for 5G networks that improves the speed and accuracy of analytics delivery by distributing inference tasks across multiple nodes, enhancing network automation.

Contribution

The paper introduces a novel hierarchical framework (H-NDAF) that separates inference and training tasks, enabling faster and more efficient analytics in 5G network management.

Findings

H-NDAF achieves faster analytics provision time.

H-NDAF provides sufficiently accurate analytics.

Simulation results validate the effectiveness of H-NDAF.

Abstract

5G introduced modularized network functions (NFs) to support emerging services in a more flexible and elastic manner. To mitigate the complexity in such modularized NF management, automated network operation and management are indispensable, and thus the 3rd generation partnership project (3GPP) has introduced a network data analytics function (NWDAF). However, a conventional NWDAF needs to conduct both inference and training tasks, and thus it is difficult to provide the analytics results to NFs in a timely manner for an increased number of analytics requests. In this article, we propose a hierarchical network data analytics framework (H-NDAF) where inference tasks are distributed to multiple leaf NWDAFs and training tasks are conducted at the root NWDAF. Extensive simulation results using open-source software (i.e., free5GC) demonstrate that H-NDAF can provide sufficiently accurate analytics and faster analytics provision time compared to the conventional NWDAF.